{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting live data from the StreamPipes data stream\n",
    "In the last tutorial ([Extracting Data from the StreamPipes data lake](../2-extracting-data-from-the-streampipes-data-lake)) we learned how to extract the stored data from a StreamPipes data lake. This tutorial is about the StreamPipes data stream and shows how to get the live data from StreamPipes into Python. Therefore, we first create the `client` instance as before."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**  \n",
    "As of now we mainly developed the support for StreamPipes functions using NATS as messaging protocol. Consequently, this setup is tested most and should work flawlessly. Visit our [first-steps](../../getting-started/first-steps) page to see how to start StreamPipes accordingly.\n",
    "Anyhow, you can also use the other [brokers](../../reference/functions/broker/broker) that are currently supported in StreamPipes Python.\n",
    "In case you observe any problems, please reach out to us and file us an [issue](https://github.com/apache/streampipes/issues) on GitHub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from streampipes.client import StreamPipesClient\n",
    "from streampipes.client.config import StreamPipesClientConfig\n",
    "from streampipes.client.credential_provider import StreamPipesApiKeyCredentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# You can install all required libraries for this tutorial with the following command\n",
    "%pip install matplotlib ipython streampipes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"SP_USERNAME\"] = \"admin@streampipes.apache.org\"\n",
    "os.environ[\"SP_API_KEY\"] = \"XXX\"\n",
    "\n",
    "# Use this if you work locally:\n",
    "os.environ[\"BROKER-HOST\"] = \"localhost\"  \n",
    "os.environ[\"KAFKA-PORT\"] = \"9094\" # When using Kafka as message broker. If Kafka is not running on localhost, KAFKA_ADVERTISED_LISTENERS should be adjusted to the external address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-12-14 10:43:37,664 - streampipes.client.client - [INFO] - [client.py:127] [_set_up_logging] - Logging successfully initialized with logging level INFO.\n"
     ]
    }
   ],
   "source": [
    "client_config = StreamPipesClientConfig(\n",
    "    credential_provider=StreamPipesApiKeyCredentials(),\n",
    "    host_address=\"localhost\",\n",
    "    port=80,\n",
    "    https_disabled=True,\n",
    ")\n",
    "client = StreamPipesClient(client_config=client_config)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can have a look at the available data streams. We can choose one or more stream to receive the data from and copy their `element_id`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-12-14 10:43:39,944 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:153] [_make_request] - Successfully retrieved all resources.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>element_id</th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>icon_url</th>\n",
       "      <th>app_id</th>\n",
       "      <th>includes_assets</th>\n",
       "      <th>includes_locales</th>\n",
       "      <th>internally_managed</th>\n",
       "      <th>measurement_object</th>\n",
       "      <th>index</th>\n",
       "      <th>...</th>\n",
       "      <th>uri</th>\n",
       "      <th>dom</th>\n",
       "      <th>num_transport_protocols</th>\n",
       "      <th>num_measurement_capability</th>\n",
       "      <th>num_application_links</th>\n",
       "      <th>num_included_assets</th>\n",
       "      <th>num_connected_to</th>\n",
       "      <th>num_category</th>\n",
       "      <th>num_event_properties</th>\n",
       "      <th>num_included_locales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>urn:streampipes.apache.org:eventstream:HHoidJ</td>\n",
       "      <td>Test2</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>urn:streampipes.apache.org:eventstream:HHoidJ</td>\n",
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       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>urn:streampipes.apache.org:eventstream:uPDKLI</td>\n",
       "      <td>Test</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      element_id   name description icon_url  \\\n",
       "0  urn:streampipes.apache.org:eventstream:HHoidJ  Test2                 None   \n",
       "1  urn:streampipes.apache.org:eventstream:uPDKLI   Test                 None   \n",
       "\n",
       "  app_id  includes_assets  includes_locales  internally_managed  \\\n",
       "0   None            False             False                True   \n",
       "1   None            False             False                True   \n",
       "\n",
       "  measurement_object  index  ...  \\\n",
       "0               None      0  ...   \n",
       "1               None      0  ...   \n",
       "\n",
       "                                             uri   dom  \\\n",
       "0  urn:streampipes.apache.org:eventstream:HHoidJ  None   \n",
       "1  urn:streampipes.apache.org:eventstream:uPDKLI  None   \n",
       "\n",
       "  num_transport_protocols  num_measurement_capability  num_application_links  \\\n",
       "0                       1                           0                      0   \n",
       "1                       1                           0                      0   \n",
       "\n",
       "   num_included_assets  num_connected_to  num_category  num_event_properties  \\\n",
       "0                    0                 0             0                     7   \n",
       "1                    0                 0             0                     7   \n",
       "\n",
       "   num_included_locales  \n",
       "0                     0  \n",
       "1                     0  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.dataStreamApi.all().to_pandas()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we can create a StreamPipesFunction. For this we need to implement the 3 following methods:\n",
    "- `onServiceStarted` is called when the function gets started. There you can use the given meta information of the `FunctionContext` to initialize the function.\n",
    "- `onEvent` is called when ever a new event arrives. The `event` contains the live data and you can use the `streamId` to identify a stream if the function is connected to multiple data streams.\n",
    "- `onServiceStopped` is called when the function gets stopped.\n",
    "\n",
    "For this tutorial we just create a function that saves every new event in a `pandas DataFrame` and plots the first column of the `DataFrame` when the function gets stopped.  \n",
    "  \n",
    "(If you want to use the same structure as in Java you can overwrite the `getFunctionId` and `requiredStreamIds` methods instead of using the `FunctionDefinition`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Dict, Any\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from streampipes.functions.function_handler import FunctionHandler\n",
    "from streampipes.functions.registration import Registration\n",
    "from streampipes.functions.streampipes_function import StreamPipesFunction\n",
    "from streampipes.functions.utils.function_context import FunctionContext\n",
    "from streampipes.model.resource.function_definition import FunctionDefinition, FunctionId\n",
    "\n",
    "class ExampleFunction(StreamPipesFunction):\n",
    "    def __init__(self, function_definition: FunctionDefinition) -> None:\n",
    "        super().__init__(function_definition)\n",
    "        # Create the Dataframe to save the live data\n",
    "        self.df = pd.DataFrame()\n",
    "\n",
    "    def onServiceStarted(self, context: FunctionContext):\n",
    "        # Get the name of the timestamp field\n",
    "        for event_property in context.schema[context.streams[0]].event_schema.event_properties:\n",
    "            if event_property.property_scope == \"HEADER_PROPERTY\":\n",
    "                self.timestamp = event_property.runtime_name\n",
    "\n",
    "    def onEvent(self, event: Dict[str, Any], streamId: str):\n",
    "        # Convert the unix timestamp to datetime\n",
    "        event[self.timestamp] = datetime.fromtimestamp(event[self.timestamp] / 1000)\n",
    "        # Add every value of the event to the DataFrame\n",
    "        self.df = pd.concat(\n",
    "            [self.df, pd.DataFrame({key: [event[key]] for key in event.keys()}).set_index(self.timestamp)]\n",
    "        )\n",
    "\n",
    "    def onServiceStopped(self):\n",
    "        # Plot the first column of the Dataframe\n",
    "        plt.figure(figsize=(10, 5))\n",
    "        plt.xlabel(self.timestamp)\n",
    "        plt.ylabel(self.df.columns[0])\n",
    "        plt.plot(self.df.iloc[:, 0])\n",
    "        plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can start the function. First we create an instance of the `ExampleFunction` and insert the `element_id` of the stream which data we want to consume. Then we have to register this function and we can start all functions by initializing the `FunctionHandler`. (it's also possible to register multiple functions with `.register(...).register(...)`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-12-14 10:43:42,810 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:153] [_make_request] - Successfully retrieved all resources.\n",
      "2022-12-14 10:43:42,812 - streampipes.functions.function_handler - [INFO] - [function_handler.py:82] [initializeFunctions] - Using NatsBroker for <__main__.ExampleFunction object at 0x000001F2EF298D90>\n"
     ]
    }
   ],
   "source": [
    "example_function = ExampleFunction(\n",
    "    FunctionDefinition(\n",
    "        function_id=FunctionId(id=\"example-function\"),\n",
    "        consumed_streams=[\"urn:streampipes.apache.org:eventstream:uPDKLI\"]\n",
    "    )\n",
    ")\n",
    "\n",
    "registration = Registration()\n",
    "registration.register(example_function)\n",
    "\n",
    "function_handler = FunctionHandler(registration, client)\n",
    "function_handler.initializeFunctions()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `DataFrame` of the `ExampleFunction` gets updated whenever a new event arrives. Let's test this by executing the cell below.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mass_flow</th>\n",
       "      <th>density</th>\n",
       "      <th>volume_flow</th>\n",
       "      <th>sensor_fault_flags</th>\n",
       "      <th>temperature</th>\n",
       "      <th>sensorId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-12-14 10:43:43.357</th>\n",
       "      <td>10.955496</td>\n",
       "      <td>47.546290</td>\n",
       "      <td>1.001985</td>\n",
       "      <td>False</td>\n",
       "      <td>44.993413</td>\n",
       "      <td>flowrate02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-14 10:43:44.371</th>\n",
       "      <td>6.499040</td>\n",
       "      <td>44.392069</td>\n",
       "      <td>2.034402</td>\n",
       "      <td>False</td>\n",
       "      <td>41.232352</td>\n",
       "      <td>flowrate02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-14 10:43:45.382</th>\n",
       "      <td>10.168300</td>\n",
       "      <td>41.192146</td>\n",
       "      <td>9.724287</td>\n",
       "      <td>False</td>\n",
       "      <td>46.812779</td>\n",
       "      <td>flowrate02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-14 10:43:46.395</th>\n",
       "      <td>10.849059</td>\n",
       "      <td>50.086308</td>\n",
       "      <td>5.832691</td>\n",
       "      <td>False</td>\n",
       "      <td>45.860412</td>\n",
       "      <td>flowrate02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-14 10:43:47.410</th>\n",
       "      <td>3.081855</td>\n",
       "      <td>47.254246</td>\n",
       "      <td>8.860531</td>\n",
       "      <td>False</td>\n",
       "      <td>50.505801</td>\n",
       "      <td>flowrate02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-14 10:44:43.920</th>\n",
       "      <td>1.803572</td>\n",
       "      <td>41.978894</td>\n",
       "      <td>10.294002</td>\n",
       "      <td>False</td>\n",
       "      <td>47.820239</td>\n",
       "      <td>flowrate02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-14 10:44:44.932</th>\n",
       "      <td>1.967062</td>\n",
       "      <td>42.212883</td>\n",
       "      <td>3.237440</td>\n",
       "      <td>False</td>\n",
       "      <td>49.047258</td>\n",
       "      <td>flowrate02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-14 10:44:45.934</th>\n",
       "      <td>4.457819</td>\n",
       "      <td>47.561256</td>\n",
       "      <td>0.315024</td>\n",
       "      <td>False</td>\n",
       "      <td>40.223413</td>\n",
       "      <td>flowrate02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-14 10:44:46.949</th>\n",
       "      <td>8.745343</td>\n",
       "      <td>46.346891</td>\n",
       "      <td>7.439090</td>\n",
       "      <td>False</td>\n",
       "      <td>41.982529</td>\n",
       "      <td>flowrate02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-14 10:44:47.950</th>\n",
       "      <td>5.828744</td>\n",
       "      <td>47.679720</td>\n",
       "      <td>6.307405</td>\n",
       "      <td>False</td>\n",
       "      <td>42.100354</td>\n",
       "      <td>flowrate02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         mass_flow    density  volume_flow  \\\n",
       "timestamp                                                    \n",
       "2022-12-14 10:43:43.357  10.955496  47.546290     1.001985   \n",
       "2022-12-14 10:43:44.371   6.499040  44.392069     2.034402   \n",
       "2022-12-14 10:43:45.382  10.168300  41.192146     9.724287   \n",
       "2022-12-14 10:43:46.395  10.849059  50.086308     5.832691   \n",
       "2022-12-14 10:43:47.410   3.081855  47.254246     8.860531   \n",
       "...                            ...        ...          ...   \n",
       "2022-12-14 10:44:43.920   1.803572  41.978894    10.294002   \n",
       "2022-12-14 10:44:44.932   1.967062  42.212883     3.237440   \n",
       "2022-12-14 10:44:45.934   4.457819  47.561256     0.315024   \n",
       "2022-12-14 10:44:46.949   8.745343  46.346891     7.439090   \n",
       "2022-12-14 10:44:47.950   5.828744  47.679720     6.307405   \n",
       "\n",
       "                         sensor_fault_flags  temperature    sensorId  \n",
       "timestamp                                                             \n",
       "2022-12-14 10:43:43.357               False    44.993413  flowrate02  \n",
       "2022-12-14 10:43:44.371               False    41.232352  flowrate02  \n",
       "2022-12-14 10:43:45.382               False    46.812779  flowrate02  \n",
       "2022-12-14 10:43:46.395               False    45.860412  flowrate02  \n",
       "2022-12-14 10:43:47.410               False    50.505801  flowrate02  \n",
       "...                                     ...          ...         ...  \n",
       "2022-12-14 10:44:43.920               False    47.820239  flowrate02  \n",
       "2022-12-14 10:44:44.932               False    49.047258  flowrate02  \n",
       "2022-12-14 10:44:45.934               False    40.223413  flowrate02  \n",
       "2022-12-14 10:44:46.949               False    41.982529  flowrate02  \n",
       "2022-12-14 10:44:47.950               False    42.100354  flowrate02  \n",
       "\n",
       "[65 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import asyncio\n",
    "from asyncio.exceptions import CancelledError\n",
    "from IPython.display import clear_output\n",
    "\n",
    "while True:\n",
    "    clear_output(wait=True)\n",
    "    display(example_function.df)\n",
    "    try:\n",
    "        await asyncio.sleep(1)\n",
    "    except CancelledError:\n",
    "        break"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `while` loop just displays the the `DataFrame` every second until the cell is stopped. We could achieve the same result manually by executing `example_function.df` repeatedly."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can stop the functions whenever you want by executing the command below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-12-14 10:44:53,309 - streampipes.functions.broker.nats_broker - [INFO] - [nats_broker.py:67] [disconnect] - Stopped connection to stream: urn:streampipes.apache.org:eventstream:uPDKLI\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "function_handler.disconnect()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's enough for this tutorial. Now you can try to write your own `StreamPipesFunction`. All you need to do is creating a new class, implementing the 4 required methods and registering the function.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Want to see more exciting use cases you can achieve with StreamPipes functions in Python? Then don't hesitate and jump to our [next tutorial](../4-using-online-machine-learning-on-a-streampipes-data-stream) on applying online machine learning algorithms to StreamPipes data streams with [River](https://riverml.xyz)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do you like this tutorial?\n",
    "We hope you like it and would love to receive some feedback from you.\n",
    "Just go to our [GitHub discussion page](https://github.com/apache/streampipes/discussions) and let us know your impression.\n",
    "We'll read and react to them all, we promise!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.7 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  },
  "license": "https://www.apache.org/licenses/LICENSE-2.0",
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "5b0c980f854fedc946bf92df903f8f9d5dc7805e8fffb7a1ceb21db6f8b983e3"
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 },
 "nbformat": 4,
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}
